Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of performing time domain channel estimation in a multi-user multiple input multiple output (“MIMO”) wireless network, the method comprising: receiving data corresponding to transmission of training signals from a plurality of users to a base station over a MIMO wireless network; determining a limited data set by limiting the received data in a time domain according to an estimated maximum delay spread; forming a well-conditioned low rank training matrix by identifying a channel model; estimating an active tap from the formed well-conditioned low rank training matrix; and subtracting a contribution of the selected active tap from the limited data set.
This invention relates to time domain channel estimation in multi-user MIMO wireless networks, addressing the challenge of accurately estimating wireless channels in environments with multiple users and antennas. The method improves channel estimation by reducing computational complexity and enhancing accuracy in the presence of multipath interference. The process begins by receiving data from multiple users transmitting training signals to a base station over a MIMO network. The received data is then filtered to create a limited data set by applying a time-domain constraint based on an estimated maximum delay spread, which helps focus on relevant signal components. A well-conditioned low-rank training matrix is formed by identifying a channel model that captures the key characteristics of the wireless channel. This matrix is used to estimate an active tap, which represents a significant signal path. The contribution of this active tap is then subtracted from the limited data set, refining the remaining signal for further processing. This iterative approach improves channel estimation by progressively isolating and removing dominant signal components, leading to more accurate channel characterization in multi-user MIMO systems. The method reduces computational overhead while maintaining estimation accuracy, making it suitable for real-time wireless communication applications.
2. The method of claim 1 , wherein estimating an active tap from the formed well-conditioned low rank training matrix comprises: selecting an active tap from the limited data set; and determining a number of active taps in the MIMO multi-user wireless network.
This invention relates to wireless communication systems, specifically methods for estimating active taps in a multiple-input multiple-output (MIMO) multi-user wireless network. The problem addressed is the challenge of accurately identifying active communication taps in such networks, particularly when dealing with limited training data. The solution involves forming a well-conditioned low-rank training matrix from the available data to improve estimation accuracy. The method begins by selecting an active tap from a limited data set, which is used to construct the training matrix. This matrix is designed to be low-rank, meaning it has fewer linearly independent rows or columns than its dimensions, which helps reduce computational complexity and noise sensitivity. The matrix is also well-conditioned, ensuring numerical stability during calculations. From this matrix, the number of active taps in the MIMO network is determined. This estimation is crucial for optimizing signal processing, resource allocation, and overall network performance in multi-user environments. The approach leverages the structure of the training data to enhance reliability in scenarios where data is scarce or noisy.
3. The method of claim 2 , wherein the active tap is selected by at least one of orthogonal matching pursuit and basis pursuit.
This invention relates to signal processing techniques for selecting an active tap in a system, particularly in applications like communications or signal reconstruction. The problem addressed is efficiently identifying the most relevant or dominant signal components (taps) from a set of potential candidates, which is critical for tasks such as channel estimation, interference suppression, or sparse signal recovery. The method involves using advanced signal processing algorithms to determine the active tap. Specifically, the selection is performed by applying either orthogonal matching pursuit (OMP) or basis pursuit (BP). OMP is an iterative greedy algorithm that progressively identifies the most significant components by projecting the signal onto a dictionary of basis functions, refining the selection at each step. Basis pursuit, on the other hand, is a convex optimization approach that reconstructs the signal by solving an l1-minimization problem, promoting sparsity in the solution. The method leverages these algorithms to enhance accuracy and efficiency in tap selection, particularly in scenarios where the signal is sparse or the number of potential taps is large. By employing OMP or BP, the system can effectively isolate the most influential signal components, improving performance in applications such as wireless communications, radar, or audio processing. The approach ensures robustness against noise and interference while maintaining computational efficiency.
4. The method of claim 2 , wherein selecting an active tap includes selecting a plurality of active taps.
A method for optimizing signal processing in a communication system involves selecting multiple active taps to enhance performance. The system operates in a domain where signal integrity is critical, particularly in environments with multipath interference or noise. The problem addressed is the degradation of signal quality due to suboptimal tap selection, which can lead to errors in data transmission. The method includes analyzing signal characteristics to identify optimal tap configurations. By selecting a plurality of active taps, the system improves signal reconstruction accuracy and reduces distortion. Each active tap corresponds to a specific time delay or phase shift, allowing the system to capture multiple signal reflections or paths. The selection process may involve evaluating signal strength, phase alignment, or other quality metrics to determine the best combination of taps. This approach is particularly useful in applications such as wireless communication, radar systems, or digital signal processing, where precise timing and phase alignment are essential. By dynamically adjusting the number and position of active taps, the system adapts to changing environmental conditions, ensuring reliable signal transmission. The method may also include filtering or weighting the selected taps to further enhance signal clarity. Overall, the technique provides a robust solution for improving signal fidelity in complex communication environments.
5. The method of claim 4 , wherein the plurality of active taps is selected according to which active taps have the strongest correlation to the received training signal.
A method for optimizing signal processing in communication systems involves selecting a subset of active taps from a plurality of taps in an adaptive filter. The selection is based on identifying which taps exhibit the strongest correlation to a received training signal. This approach improves signal quality by focusing computational resources on the most relevant taps, reducing interference and enhancing performance. The adaptive filter adjusts its coefficients to minimize error between the filtered output and a desired signal, using the selected taps to refine the filtering process. The method is particularly useful in environments with multipath interference or fading, where signal distortion is common. By dynamically selecting the most correlated taps, the system adapts efficiently to changing channel conditions, ensuring reliable communication. The technique can be applied in wireless networks, digital signal processing, and other areas where adaptive filtering is used to mitigate signal degradation. The selection process may involve comparing correlation metrics or using statistical methods to determine the strongest relationships between the taps and the training signal. This ensures optimal performance while minimizing computational overhead.
6. The method of claim 4 , wherein for each user the plurality of active taps includes at least one active tap corresponding to that user.
Network communication. This invention addresses the problem of efficiently managing network resources and ensuring personalized service for users in a system involving active taps for data flow. The technology relates to a system and method for managing active taps in a network. The core concept involves a plurality of active taps, which are essentially conduits or connections used for data flow. A key feature is that for every individual user within the system, there must be at least one active tap specifically designated and associated with that user. This ensures that each user has a dedicated or at least one available active tap, facilitating personalized management of their network traffic or data stream. This configuration likely aims to provide differentiated service, quality of service guarantees, or tailored resource allocation based on individual user needs and network activity. The presence of user-specific active taps implies a mechanism for identifying users and dynamically assigning or maintaining these taps to them.
7. The method of claim 2 , wherein selecting an active tap from the limited data set comprises: detecting active tap locations of the reduced data set; and selecting the active tap according to the estimated active tap locations.
This invention relates to signal processing, specifically methods for selecting an active tap in a reduced data set to improve performance in communication systems or signal analysis. The problem addressed is the need to accurately identify and select active taps from a limited data set, which is often necessary in applications like channel estimation, equalization, or interference cancellation where computational efficiency and accuracy are critical. The method involves first detecting active tap locations within a reduced data set, which has been preprocessed to contain only relevant signal components. The detection process identifies positions in the data where significant signal activity occurs, distinguishing them from noise or irrelevant data. Once these active tap locations are estimated, the method selects the most appropriate active tap based on these detected positions. This selection ensures that the chosen tap corresponds to actual signal activity, improving the reliability of subsequent processing steps. The reduced data set is derived from an original data set by applying a reduction technique, such as downsampling or dimensionality reduction, to retain only the most informative portions of the signal. The active tap selection is performed on this reduced set to balance computational efficiency with accuracy. By focusing on the detected active locations, the method avoids unnecessary processing of irrelevant data, enhancing overall system performance. This approach is particularly useful in real-time applications where rapid and accurate signal analysis is required.
8. The method of claim 7 , wherein the active tap locations are detected using a matching pursuit algorithm.
A method for detecting active tap locations in a system involves using a matching pursuit algorithm to identify these locations. The system likely relates to signal processing or acoustic analysis, where the goal is to determine specific points where a user interacts with a surface, such as a touchscreen or a touch-sensitive panel. The matching pursuit algorithm is a computational technique that decomposes a signal into a linear combination of waveforms, allowing for precise localization of events like taps. This approach improves accuracy by isolating relevant signal components from background noise or interference. The method may be part of a broader system for touch detection, gesture recognition, or user input processing, where identifying exact tap locations is critical for functionality. The use of matching pursuit ensures robustness in noisy environments and enhances the reliability of the system's response to user interactions. This technique is particularly useful in applications requiring high precision, such as medical devices, industrial control interfaces, or advanced consumer electronics. The method may also include preprocessing steps to condition the input signal before applying the matching pursuit algorithm, ensuring optimal performance. Overall, the invention provides an efficient and accurate way to detect active tap locations in a variety of touch-sensitive applications.
9. The method of claim 7 , wherein the active tap is selected by solving a l1/l2 norm minimization problem.
This invention relates to signal processing techniques for selecting an active tap in a system, such as an adaptive filter or communication system, where the goal is to identify the most relevant or dominant signal component from multiple possible inputs. The problem addressed is the efficient and accurate selection of the active tap, which is crucial for tasks like channel estimation, interference cancellation, or beamforming in wireless communications. Traditional methods may rely on heuristic approaches or brute-force searches, which can be computationally expensive or suboptimal. The invention improves upon prior methods by selecting the active tap through solving an l1/l2 norm minimization problem. This mathematical optimization approach leverages the sparsity of the signal or system response, meaning it identifies the most significant tap while suppressing irrelevant or noise-dominated components. The l1 norm promotes sparsity by encouraging zero or near-zero values for non-active taps, while the l2 norm ensures stability and smoothness in the solution. By combining these norms, the method achieves a balance between sparsity and robustness, leading to more accurate and efficient tap selection. The solution involves formulating the tap selection as an optimization problem where the objective is to minimize a weighted combination of the l1 and l2 norms of the tap coefficients. The weights can be adjusted based on the specific application or signal characteristics. This approach is particularly useful in scenarios where the system has a sparse impulse response, such as in multipath fading channels or sparse interference environments. The method can be implemented using convex optimization techniques, such as least absolute shrinkage and selection operator (LASSO) o
10. The method of claim 2 , wherein selecting an active tap from the limited data set comprises applying a basis pursuit de-noising function associated with an l1-norm objective function to the reduced data set.
This invention relates to signal processing techniques for selecting an active tap from a limited data set in communication systems, particularly in scenarios where signal recovery is challenging due to noise or interference. The method addresses the problem of accurately identifying and extracting relevant signal components from a corrupted or incomplete data set, which is critical for applications such as wireless communications, radar systems, and signal reconstruction. The process involves first reducing the data set to a smaller, more manageable subset while preserving essential signal characteristics. This reduced data set is then processed using a basis pursuit de-noising function, which leverages an l1-norm objective function to optimize signal recovery. The l1-norm objective function promotes sparsity, meaning it prioritizes solutions with fewer non-zero coefficients, which is particularly effective in noisy environments where irrelevant or redundant data points can obscure the true signal. By applying this approach, the method enhances the accuracy and reliability of signal extraction, even when dealing with limited or degraded data. The use of basis pursuit de-noising ensures that the selected active tap is the most representative of the original signal, minimizing errors introduced by noise or interference. This technique is particularly valuable in applications where signal integrity is paramount, such as high-speed data transmission, sensor networks, and medical imaging.
11. The method of claim 2 , comprising repeating the steps of selecting and subtracting until a residual signal norm falls below a specified minimum.
This invention relates to signal processing techniques for reducing or eliminating unwanted components from a signal. The problem addressed is the presence of noise, interference, or other undesirable signal components that degrade signal quality in applications such as communications, audio processing, or sensor data analysis. The method involves iteratively selecting and subtracting signal components to refine the remaining signal until it meets a predefined quality threshold. The process begins by analyzing the input signal to identify dominant or unwanted components. These components are then systematically removed through subtraction, reducing the signal's residual error. The subtraction step is repeated in successive iterations, each time refining the residual signal further. The iteration continues until the norm (a mathematical measure of the signal's magnitude) of the residual signal falls below a specified minimum threshold, indicating that the remaining signal meets the desired quality criteria. This iterative approach ensures that the final output signal is free of significant unwanted components while preserving the integrity of the desired signal. The method is particularly useful in applications where precise signal reconstruction or noise reduction is critical, such as in digital communications, audio enhancement, or biomedical signal processing. By dynamically adjusting the subtraction process based on the residual signal's norm, the technique adapts to varying signal conditions, ensuring robust performance across different scenarios.
12. The method of claim 1 , wherein at least one of the training signals is a sounding reference signal (“SRS”) signal.
This invention relates to wireless communication systems, specifically methods for generating and using training signals to improve signal transmission quality. The problem addressed is the need for accurate channel estimation and feedback in wireless networks to enhance data transmission efficiency and reliability. The method involves generating training signals, such as sounding reference signals (SRS), to assess the communication channel between a transmitter and receiver. These signals are used to estimate channel conditions, allowing the system to adapt transmission parameters like power, modulation, and coding to optimize performance. The SRS signals are transmitted from a user device to a base station, enabling the base station to evaluate the uplink channel quality and provide feedback for adjustments. The method may also include generating other types of training signals, such as demodulation reference signals (DMRS), which are used for demodulating data signals. The combination of SRS and other training signals allows for comprehensive channel estimation, improving both uplink and downlink transmissions. The system dynamically adjusts transmission parameters based on the feedback derived from these signals, ensuring robust communication in varying channel conditions. This approach enhances spectral efficiency and reduces errors in wireless communication, particularly in environments with high interference or mobility. The use of SRS signals as part of the training process ensures accurate channel state information, leading to better resource allocation and overall system performance.
13. The method of claim 1 , wherein at least one of the training signals is a demodulation reference signal (“DMRS”) signal.
This invention relates to wireless communication systems, specifically methods for generating and processing training signals to improve signal demodulation and channel estimation. The problem addressed is the need for accurate and efficient reference signals in wireless transmissions to enhance data detection and decoding, particularly in environments with interference or multipath effects. The method involves generating training signals for use in wireless communication, where at least one of these signals is a demodulation reference signal (DMRS). DMRS signals are used to assist in the demodulation of transmitted data by providing known reference points that help receivers estimate the channel conditions and correct for distortions. The method may also include generating other types of training signals, such as synchronization signals or channel state information reference signals (CSI-RS), depending on the communication protocol. The DMRS signal is designed to be embedded within the transmitted data, allowing the receiver to track changes in the channel over time. This is particularly useful in high-mobility scenarios or frequency-selective channels where the signal quality can vary rapidly. The method ensures that the DMRS is properly configured in terms of its sequence, time-frequency allocation, and power level to optimize performance. By incorporating DMRS into the training signal set, the invention improves the reliability of data demodulation, reduces errors, and enhances overall communication efficiency in wireless networks. The approach is applicable to various wireless standards, including 5G and beyond, where precise channel estimation is critical for high-speed data transmission.
14. The method of claim 1 , wherein the MIMO wireless network is a cloud radio access network (“C-RAN”) network.
A wireless communication system employs multiple-input multiple-output (MIMO) technology to enhance data transmission efficiency and reliability. In such systems, multiple antennas at both the transmitter and receiver improve signal quality and capacity. However, traditional MIMO networks face challenges in centralized coordination, resource management, and scalability, particularly in dense urban environments where high data demands and interference are prevalent. To address these issues, the system integrates MIMO technology with a cloud radio access network (C-RAN) architecture. In a C-RAN network, baseband processing is centralized in a cloud-based pool, while remote radio heads (RRHs) handle radio frequency (RF) functions. This separation allows for dynamic resource allocation, reduced latency, and improved coordination among distributed antennas. The centralized processing enables advanced signal processing techniques, such as joint transmission and reception, to mitigate interference and optimize performance across the network. The system further includes mechanisms for real-time monitoring and adaptation of MIMO configurations based on network conditions. By leveraging cloud computing, the network can efficiently manage high data traffic loads, support diverse services, and adapt to varying user demands. This approach enhances spectral efficiency, reduces operational costs, and improves overall network performance in complex wireless environments.
15. The method of claim 1 , wherein the network operates using at least one of time division duplexed (“TDD”) and frequency division duplexed (“FDD”) communications.
A method for wireless communication involves managing network operations in a system that supports both time division duplexed (TDD) and frequency division duplexed (FDD) communications. The method includes dynamically adjusting transmission parameters to optimize performance based on network conditions, such as interference levels, traffic load, and user device capabilities. This adjustment may involve switching between TDD and FDD modes or configuring hybrid configurations where both modes coexist. The system monitors signal quality, latency, and throughput to determine the most efficient communication mode for different network segments or user devices. Additionally, the method may involve coordinating with neighboring cells to minimize interference and ensure seamless handover between TDD and FDD regions. The approach aims to improve spectral efficiency, reduce latency, and enhance overall network reliability by leveraging the strengths of both duplexing techniques in a flexible manner. The solution is particularly useful in heterogeneous networks where different duplexing schemes may be required to support diverse service demands and device types.
16. The method of claim 1 , wherein the active tap is estimated using at least one of: l1-norm minimization; l2-norm minimization; regulated L2-norm immunization; OMP greedy matching pursuit; and stomp greedy matching pursuit.
This invention relates to signal processing techniques for estimating an active tap in a communication system, particularly in scenarios where sparse channel responses are present. The problem addressed is the efficient and accurate estimation of the active tap in a sparse channel, which is crucial for applications such as wireless communications, radar, and signal reconstruction. Traditional methods often struggle with computational complexity or accuracy in sparse environments. The method involves estimating the active tap using one or more optimization techniques. These techniques include l1-norm minimization, which promotes sparsity by minimizing the sum of absolute values of the coefficients; l2-norm minimization, which minimizes the sum of squared coefficients; and regulated L2-norm minimization, which introduces constraints to improve robustness. Additionally, greedy matching pursuit algorithms like Orthogonal Matching Pursuit (OMP) and StOchastic Matching Pursuit (StOMP) are employed. OMP iteratively selects the most correlated components, while StOMP uses stochastic sampling to identify significant coefficients. These methods collectively enhance the accuracy and efficiency of active tap estimation in sparse channel environments.
17. A system for performing time domain channel estimation in a multi-user multiple input multiple output (“MIMO”) wireless network, the system comprising: a signal receiving unit configured to receive data corresponding to transmission of training signals from a plurality of users to a base station over a MIMO wireless network; and a signal processing unit configured to receive data corresponding to transmission of training signals from a plurality of users to a base station over a MIMO wireless network; determine a limited data set by limiting the received data in a time domain according to an estimated maximum delay spread; form a well-conditioned low rank training matrix by identifying a channel model; estimate an active tap from the formed well-conditioned low rank training matrix; and subtract a contribution of the selected active tap from the limited data set.
A system performs time domain channel estimation in a multi-user MIMO wireless network to improve signal processing accuracy. In such networks, multiple users transmit signals to a base station simultaneously, and accurate channel estimation is critical for reliable communication. The system addresses challenges in estimating channel responses by processing training signals transmitted from multiple users to a base station. A signal receiving unit captures the transmitted training signals, while a signal processing unit processes this data. The processing unit first limits the received data in the time domain based on an estimated maximum delay spread, reducing computational complexity. It then forms a well-conditioned low-rank training matrix by identifying a channel model, which helps in mitigating noise and interference. The system estimates an active tap from this matrix, representing the most significant channel response, and subtracts its contribution from the limited data set. This iterative process refines channel estimation by progressively removing dominant signal components, enhancing accuracy in multi-user MIMO environments. The approach improves signal detection and decoding by accurately modeling channel characteristics in the time domain.
18. The system of claim 17 , wherein the signal processing unit is further configured to estimate an active tap from the formed well-conditioned low rank training matrix by: selecting an active tap from the limited data set; and determining a number of active taps in the MIMO multi-user wireless network.
A system for signal processing in a MIMO multi-user wireless network addresses the challenge of efficiently estimating active communication channels (taps) in environments with limited data. The system includes a signal processing unit that forms a well-conditioned low-rank training matrix from received signals, improving the reliability of channel estimation. The unit further estimates active taps by selecting a tap from the limited data set and determining the number of active taps in the network. This approach enhances accuracy in identifying active communication paths, particularly in scenarios with sparse or noisy data, thereby optimizing network performance and resource allocation. The system leverages low-rank matrix techniques to reduce computational complexity while maintaining robust channel estimation, making it suitable for dynamic wireless environments with multiple users.
19. The system of claim 18 , wherein the signal processing unit is further configured to repeatedly select the active tap and subtract the contribution until a residual signal norm falls below a specified minimum.
This invention relates to signal processing systems, specifically for adaptive filtering or interference cancellation. The system addresses the challenge of efficiently reducing unwanted signal components, such as noise or interference, from a desired signal. The system includes a signal processing unit that dynamically adjusts filter parameters to minimize residual error. The unit repeatedly selects an active tap, which represents a filter coefficient or weight, and subtracts its contribution from the input signal. This process continues iteratively until the norm of the residual signal—the difference between the filtered output and the desired signal—falls below a predefined threshold. The system ensures convergence by adaptively updating the active tap based on the residual signal, improving signal quality over time. The method is particularly useful in applications like communication systems, radar, and audio processing where real-time interference suppression is critical. The iterative approach optimizes computational efficiency while maintaining accuracy, making it suitable for resource-constrained environments. The system may also include additional components, such as an input interface for receiving the signal and an output interface for delivering the processed signal. The adaptive filtering technique ensures robust performance in dynamic environments where interference characteristics change over time.
20. The system of claim 18 , wherein the signal processing unit is configured to select the active tap by at least one of orthogonal matching pursuit and basis pursuit.
This invention relates to signal processing systems for selecting an active tap in a signal processing unit. The system addresses the challenge of efficiently identifying and processing relevant signal components in complex environments, such as wireless communications, radar, or sensor networks, where interference and noise can degrade performance. The signal processing unit is designed to extract meaningful signal information by selecting an active tap, which represents a specific time delay or frequency component of the input signal. The selection process is optimized using advanced signal reconstruction techniques, specifically orthogonal matching pursuit (OMP) or basis pursuit (BP). OMP is an iterative algorithm that identifies the most significant signal components by projecting the signal onto a dictionary of basis functions, while BP is a convex optimization method that reconstructs the signal by minimizing its sparsity under certain constraints. These techniques enhance the system's ability to accurately recover sparse signals, improving signal quality and reducing computational overhead. The system may also include additional components, such as an analog-to-digital converter for digitizing the input signal and a memory for storing signal data, ensuring efficient processing and real-time operation. The invention is particularly useful in applications requiring high precision and robustness in signal analysis.
21. The system of claim 18 , wherein the signal processing unit is configured to select a plurality of active taps.
The system relates to signal processing, specifically for optimizing the performance of adaptive filters used in communication systems. Adaptive filters adjust their coefficients to minimize errors in signal estimation, but their performance can degrade due to noise, interference, or dynamic channel conditions. The system addresses this by dynamically selecting a subset of active filter taps to improve signal quality and reduce computational complexity. The signal processing unit within the system evaluates the filter taps and identifies a plurality of active taps that contribute most significantly to signal reconstruction. These selected taps are prioritized for processing, while less significant taps are deactivated or weighted lower. This selective activation reduces computational overhead and enhances filter efficiency, particularly in environments with high interference or rapidly changing channel conditions. The system may also include a feedback mechanism to continuously assess tap contributions and adjust the active tap selection in real-time. This adaptive approach ensures optimal performance under varying signal conditions. The method is applicable to various communication technologies, including wireless, wired, and satellite systems, where signal integrity and processing efficiency are critical. By dynamically managing filter taps, the system improves signal fidelity while minimizing resource consumption.
22. The system of claim 21 , wherein the signal processing unit is configured to select the plurality of active taps according to which active taps have the strongest correlation to the received training signal.
This invention relates to signal processing systems, particularly for optimizing the selection of active taps in an adaptive filter to improve signal correlation. The system addresses the challenge of efficiently identifying and utilizing the most relevant filter taps to enhance signal quality in communication or signal processing applications. The system includes an adaptive filter with a plurality of taps, where each tap represents a potential signal correlation point. A signal processing unit dynamically selects a subset of these taps as "active taps" based on their correlation strength with a received training signal. The selection process involves evaluating the correlation metrics of each tap and prioritizing those with the strongest alignment to the training signal. This adaptive selection improves the filter's ability to accurately reconstruct or process the desired signal by focusing computational resources on the most significant taps. The system may also include a training signal generator to provide reference signals for correlation analysis, and a feedback mechanism to adjust tap weights based on the selected active taps. By dynamically adjusting the active taps, the system enhances signal fidelity, reduces computational overhead, and improves overall performance in applications such as wireless communications, radar, or audio processing. The invention ensures efficient resource utilization while maintaining high signal accuracy.
23. The system of claim 21 , wherein the signal processing unit is configured to select the plurality of active taps such that, for each user, the plurality of active taps includes at least one active tap corresponding to that user.
This invention relates to signal processing in communication systems, specifically for managing active taps in a multi-user environment. The problem addressed is optimizing signal processing to ensure each user in a communication system receives dedicated processing resources while minimizing computational overhead. The system includes a signal processing unit that dynamically selects a plurality of active taps from a larger set of available taps. The selection is configured such that for each user in the system, at least one active tap is specifically assigned to that user. This ensures that each user's signals are processed with dedicated resources, improving signal quality and reducing interference. The system may also include a tap selection module that determines the optimal set of active taps based on factors such as signal strength, user priority, or channel conditions. By dynamically adjusting the active taps, the system adapts to changing conditions, maintaining efficient resource allocation while ensuring reliable communication for all users. The invention is particularly useful in wireless communication systems where multiple users share the same frequency resources, and efficient tap management is critical for performance.
24. The system of claim 18 , wherein the signal processing unit is configured to select and estimate the active tap by: determining a number of active taps in the wireless network; identifying a channel model; forming a well-conditioned low rank training matrix; and estimating the active tap from the low rank training matrix.
This invention relates to wireless communication systems, specifically improving signal processing in multi-tap channels. The problem addressed is the challenge of accurately identifying and estimating active signal paths (taps) in wireless networks, which is critical for reliable data transmission. The system includes a signal processing unit that enhances tap estimation by first determining the number of active taps in the network. It then identifies a channel model to represent the signal propagation characteristics. Using this model, the system forms a well-conditioned low-rank training matrix, which simplifies the estimation process by reducing computational complexity while maintaining accuracy. The active tap is then estimated from this matrix, improving signal detection and reducing errors. This approach is particularly useful in environments with multipath interference, where traditional methods struggle with high-dimensional data. The system ensures efficient and precise channel estimation, leading to better performance in wireless communications.
25. The system of claim 18 , wherein the signal processing unit is configured to select the active tap by: detecting active tap locations of the reduced data set; and selecting the active tap according to the estimated active tap locations.
A system for signal processing in communication or data transmission applications addresses the challenge of efficiently identifying and selecting active signal paths (taps) in a received signal, particularly in environments with sparse or intermittent data transmission. The system includes a signal processing unit that processes a received signal to generate a reduced data set, which simplifies the signal for further analysis. The unit then detects the locations of active taps within this reduced data set, representing the most significant signal paths. Based on these estimated active tap locations, the system selects the optimal active tap for further processing or transmission. This approach improves signal accuracy and reduces computational overhead by focusing only on relevant signal components. The system may also include additional components such as an analog-to-digital converter for initial signal digitization and a memory for storing processed data. The method ensures efficient signal reconstruction or decoding by dynamically adapting to the active signal paths, enhancing performance in applications like wireless communication, radar, or sensor networks.
26. The system of claim 25 , wherein the signal processing unit is configured to detect the active tap locations using a matching pursuit algorithm.
The system relates to signal processing for identifying active tap locations in a communication channel, particularly in applications like digital signal processing or wireless communications where precise detection of signal paths is critical. The problem addressed is the need for accurate and efficient detection of active tap locations, which are the points where a transmitted signal interacts with the channel, to improve signal reconstruction and communication reliability. The system includes a signal processing unit that analyzes received signals to determine the active tap locations. These locations represent the time delays or paths through which the signal travels in the channel. The signal processing unit employs a matching pursuit algorithm, a computational technique that iteratively decomposes signals into a linear combination of basis functions to identify the most significant signal components. This algorithm is particularly effective for sparse signal representations, where only a few tap locations contribute significantly to the received signal. By using the matching pursuit algorithm, the system can accurately detect the active tap locations even in noisy or complex channel conditions. This improves the accuracy of channel estimation, which is essential for tasks like equalization, beamforming, and signal reconstruction. The system may also include additional components, such as a receiver front-end for capturing the signal and a controller for managing the signal processing operations. The overall approach enhances communication performance by enabling precise identification of signal paths, leading to better signal quality and reliability.
27. The system of claim 25 , wherein the signal processing unit is configured to select the active tap by solving an l1/l2 norm minimization problem.
This invention relates to signal processing systems, particularly those used in communication or data transmission where signal distortion or interference must be mitigated. The problem addressed is the need for efficient and accurate selection of an active tap in a signal processing system, such as in adaptive filters or equalizers, to improve signal quality and reduce computational complexity. The system includes a signal processing unit that processes input signals to extract or enhance desired signal components while suppressing noise or interference. The signal processing unit is configured to select an active tap by solving an l1/l2 norm minimization problem. This mathematical optimization approach helps identify the most relevant signal components (taps) while minimizing computational overhead and ensuring robustness against noise. The l1/l2 norm minimization problem is a well-known technique in signal processing for sparse signal recovery, where only a few significant coefficients (taps) are selected from a larger set. By solving this problem, the system efficiently determines which tap should be active, improving signal reconstruction accuracy and reducing power consumption. This method is particularly useful in applications like wireless communications, radar systems, or audio processing, where real-time performance and accuracy are critical. The system may also include additional components, such as an input interface for receiving signals, an output interface for transmitting processed signals, and memory for storing intermediate results. The signal processing unit may further incorporate adaptive algorithms to dynamically adjust tap selection based on changing signal conditions. The overall design ensures high performance while maintaining computational
28. The system of claim 18 , wherein the signal processing unit is configured to select the active tap by applying a basis pursuit de-noising function associated with an l1-norm objective function to the reduced data set.
The invention relates to signal processing systems for analyzing data sets, particularly in applications where noise reduction and feature extraction are critical. The system addresses the challenge of identifying relevant data points (active taps) within a larger data set while minimizing the impact of noise and irrelevant information. The core innovation involves a signal processing unit that employs a basis pursuit de-noising function with an l1-norm objective function to select the most significant data points. This approach leverages the l1-norm's sparsity-inducing properties to isolate key features while suppressing noise, improving the accuracy and efficiency of data analysis. The system is designed to work with reduced data sets, meaning it processes a subset of the original data to enhance computational efficiency without sacrificing performance. The basis pursuit de-noising function optimizes the selection of active taps by minimizing the l1-norm of the coefficients, ensuring that only the most relevant data points are retained. This method is particularly useful in fields such as signal processing, machine learning, and data compression, where distinguishing meaningful signals from noise is essential. The system's ability to handle reduced data sets makes it suitable for real-time applications where processing speed is critical.
29. The system of claim 17 , wherein at least one of the training signals is a sounding reference signal (“SRS”) signal.
A system for wireless communication includes a transmitter and a receiver configured to exchange signals in a wireless network. The system is designed to improve signal quality and reliability in wireless communications by using training signals to estimate and compensate for channel impairments. The transmitter generates and transmits training signals, which the receiver uses to measure channel conditions and adjust its processing accordingly. The system may include multiple antennas to support spatial multiplexing, beamforming, or other advanced techniques to enhance data throughput and coverage. At least one of the training signals is a sounding reference signal (SRS), which is a specialized signal used by the receiver to estimate the uplink channel quality. The SRS allows the transmitter to adapt its transmission parameters, such as power, modulation, and coding, based on the feedback derived from the SRS. The system may also include error correction mechanisms to further improve communication reliability. The overall goal is to optimize wireless communication performance by dynamically adjusting transmission parameters in response to real-time channel conditions.
30. The system of claim 17 , wherein at least one of the training signals is a demodulation reference signal (“DMRS”) signal.
A system for wireless communication includes a transmitter and a receiver configured to exchange signals in a wireless network. The system is designed to improve signal demodulation and channel estimation in high-mobility or high-frequency environments where traditional reference signals may be insufficient. The transmitter generates and transmits training signals, including demodulation reference signals (DMRS), to assist the receiver in accurately demodulating received data. The DMRS signals are specifically structured to provide reliable channel state information, even under challenging conditions such as rapid channel variations or high path loss. The receiver processes these DMRS signals to estimate the channel characteristics and compensate for distortions, ensuring accurate data recovery. The system may also include additional features such as adaptive modulation and coding schemes, beamforming techniques, or interference mitigation mechanisms to further enhance communication reliability. The use of DMRS signals helps maintain signal integrity and reduces errors in data transmission, making the system suitable for applications requiring high-speed, low-latency communication, such as 5G and beyond-5G networks.
31. The system of claim 17 , wherein the MIMO wireless network is a cloud radio access network (“C-RAN”) network.
A system for managing a multiple-input multiple-output (MIMO) wireless network, specifically a cloud radio access network (C-RAN), is disclosed. The system addresses the challenge of efficiently coordinating distributed radio units (RRUs) and centralized baseband processing units (BBUs) in a C-RAN architecture to optimize network performance, reduce latency, and improve resource utilization. The system includes a centralized controller that dynamically allocates radio resources, manages interference, and optimizes data transmission between the RRUs and BBUs. The controller uses real-time data from the network to adjust beamforming, scheduling, and power control, ensuring high spectral efficiency and reliable connectivity. The system also supports seamless handover between RRUs, minimizing service disruptions for mobile users. By leveraging cloud computing, the BBUs can be virtualized, allowing for scalable and flexible deployment. The system further integrates machine learning algorithms to predict network conditions and proactively adjust configurations, enhancing overall network reliability and user experience. This approach enables efficient use of network resources while maintaining low-latency communication, making it suitable for applications such as 5G and beyond.
32. The system of claim 17 , wherein the network operates using time division duplexed (“TDD”) and frequency division duplexed (“FDD”) communications.
A wireless communication system is designed to support both time division duplexed (TDD) and frequency division duplexed (FDD) operations. The system includes a base station with multiple antennas configured to transmit and receive signals in both TDD and FDD modes. In TDD mode, the system dynamically allocates time slots for uplink and downlink communications on the same frequency band, allowing bidirectional communication without requiring separate frequency bands. In FDD mode, the system uses distinct frequency bands for uplink and downlink transmissions, enabling simultaneous two-way communication. The base station dynamically switches between TDD and FDD modes based on network conditions, traffic demands, or interference levels to optimize spectral efficiency and reduce latency. The system also includes user devices equipped with antennas and processing circuitry to support both TDD and FDD communications, ensuring seamless connectivity across different network configurations. This dual-mode operation enhances flexibility, improves resource utilization, and supports diverse deployment scenarios, including urban and rural environments. The system may also incorporate beamforming techniques to further enhance signal quality and coverage.
33. The system of claim 17 , wherein the signal processing unit is further configured to estimate the active tap using at least one of: l1-norm minimization; l2-norm minimization; regulated L2-norm immunization; OMP greedy matching pursuit; and stomp greedy matching pursuit.
This invention relates to signal processing systems for estimating active taps in communication channels, particularly in scenarios where sparse channel responses are present. The system addresses the challenge of accurately identifying and estimating active taps in a channel with minimal computational complexity, which is critical for efficient signal recovery in applications such as wireless communications, radar, and signal reconstruction. The system includes a signal processing unit that processes received signals to estimate the active taps in the channel. The estimation is performed using one or more optimization techniques, including l1-norm minimization, l2-norm minimization, regulated L2-norm minimization, orthogonal matching pursuit (OMP) greedy matching pursuit, or stochastic matching pursuit (StOMP) greedy matching pursuit. These methods are employed to identify the most significant taps in the channel, enabling efficient signal recovery and reducing computational overhead. The signal processing unit may also include a channel estimation module that generates an initial estimate of the channel response, which is then refined using the selected optimization technique. The system is designed to handle sparse channel responses, where only a subset of taps are active, making it suitable for applications where computational efficiency and accuracy are paramount. The use of different optimization techniques allows the system to adapt to varying signal conditions and requirements, ensuring robust performance across different scenarios.
34. A non-transitory computer readable medium storing a computer program, executable by a machine, for performing time domain channel estimation in a multi-user multiple input multiple output (“MIMO”) wireless network, the computer program comprising executable instructions for: receiving data corresponding to transmission of training signals from a plurality of users to a base station over a MIMO wireless network; determining a limited data set by limiting the received data in a time domain according to an estimated maximum delay spread; forming a well-conditioned low rank training matrix by identifying a channel model; estimating an active tap from the formed well-conditioned low rank training matrix; and subtracting a contribution of the selected active tap from the limited data set.
This invention relates to time domain channel estimation in multi-user MIMO wireless networks, addressing the challenge of accurately estimating wireless channels in complex environments with multiple users and antennas. The method involves processing training signals transmitted from multiple users to a base station over a MIMO wireless network. The received data is first filtered to create a limited data set by restricting it in the time domain based on an estimated maximum delay spread, which reduces computational complexity and noise. A well-conditioned low-rank training matrix is then formed by identifying an appropriate channel model, ensuring numerical stability and efficient processing. From this matrix, an active tap (a significant channel response component) is estimated. The contribution of this active tap is subtracted from the limited data set, refining the channel estimation process. This iterative approach improves accuracy by progressively isolating and removing dominant channel effects, enhancing overall performance in multi-user MIMO systems. The technique is implemented via a computer program stored on a non-transitory medium, enabling real-time execution in wireless communication systems. The solution optimizes channel estimation by leveraging time-domain constraints and low-rank matrix properties, reducing errors and computational overhead in dynamic wireless environments.
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June 16, 2020
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